Recommendations, such as those written on behalf of applicants seeking employment or school admission, are an important part of the hiring or admission process. These types of recommendations are typically reviewed manually by humans, which is a burdensome process that lacks objectivity and consistency. What is worse, the burden often causes recommendations to be ignored all together. This is unfortunate, as constructed recommendations provide valuable insight into an applicant's non-cognitive traits that may be predictive of success (e.g., being admitted, graduating, or excelling at work), but are not measured by traditional standardized tests (e.g., GRE or SAT). Thus, the present inventors have observed a need to automate the assessment of constructed recommendations and other constructed texts that provide opinions on particular subjects (e.g., student, job candidate, company, product, service, etc.).